Artificial intelligence in otorhinolaryngology: current trends and application areas

This study aims to perform a bibliometric analysis of scientific research on the use of artificial intelligence (AI) in the field of Otorhinolaryngology (ORL), with a specific focus on identifying emerging AI trend topics within this discipline.

A total of 498 articles on AI in ORL, published between 1982 and 2024, were retrieved from the Web of Science database. Various bibliometric techniques, including trend keyword analysis and factor analysis, were applied to analyze the data.

The most prolific journal was the European Archives of Oto-Rhino-Laryngology (n = 67). The USA (n = 200) and China (n = 61) were the most productive countries in AI-related ORL research. The most productive institutions were Harvard University / Harvard Medical School (n = 71). The leading authors in this field were Lechien JR. (n = 18) and Rameau A. (n = 17). The most frequently used keywords in the AI research were cochlear implant, head and neck cancer, magnetic resonance imaging (MRI), hearing loss, patient education, diagnosis, radiomics, surgery, hearing aids, laryngology ve otitis media. Recent trends in otorhinolaryngology research reflect a dynamic focus, progressing from hearing-related technologies such as hearing aids and cochlear implants in earlier years, to diagnostic innovations like audiometry, psychoacoustics, and narrow band imaging. The emphasis has recently shifted toward advanced applications of MRI, radiomics, and computed tomography (CT) for conditions such as head and neck cancer, chronic rhinosinusitis, laryngology, and otitis media. Additionally, increasing attention has been given to patient education, quality of life, and prognosis, underscoring a holistic approach to diagnosis, surgery, and treatment in otorhinolaryngology.

AI has significantly impacted the field of ORL, especially in diagnostic imaging and therapeutic planning. With advancements in MRI and CT-based technologies, AI has proven to enhance disease detection and management. The future of AI in ORL suggests a promising path toward improving clinical decision-making, patient care, and healthcare efficiency.